






"""simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


snake_case : Dict     		=		logging.get_logger(__name__)

snake_case : Tuple     		=		{
    """Helsinki-NLP/opus-mt-en-de""": """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json""",
    # See all Marian models at https://huggingface.co/models?filter=marian
}



class        UpperCamelCase__ (				a_):







    """simple docstring"""
    __UpperCAmelCase           = """marian"""
    __UpperCAmelCase           = ["""past_key_values"""]
    __UpperCAmelCase           = {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}







    def __init__(		self :	Optional[int]				,    UpperCamelCase_ :	List[str]=5_8_1_0_1				,    UpperCamelCase_ :	Union[str, Any]=None				,    UpperCamelCase_ :	Any=1_0_2_4				,    UpperCamelCase_ :	List[str]=1_2				,    UpperCamelCase_ :	Tuple=4_0_9_6				,    UpperCamelCase_ :	int=1_6				,    UpperCamelCase_ :	str=1_2				,    UpperCamelCase_ :	List[Any]=4_0_9_6				,    UpperCamelCase_ :	List[str]=1_6				,    UpperCamelCase_ :	int=0.0				,    UpperCamelCase_ :	str=0.0				,    UpperCamelCase_ :	Tuple=True				,    UpperCamelCase_ :	Union[str, Any]=True				,    UpperCamelCase_ :	Union[str, Any]="gelu"				,    UpperCamelCase_ :	Dict=1_0_2_4				,    UpperCamelCase_ :	Optional[Any]=0.1				,    UpperCamelCase_ :	Optional[int]=0.0				,    UpperCamelCase_ :	Union[str, Any]=0.0				,    UpperCamelCase_ :	int=0.02				,    UpperCamelCase_ :	List[str]=5_8_1_0_0				,    UpperCamelCase_ :	Any=False				,    UpperCamelCase_ :	List[Any]=5_8_1_0_0				,    UpperCamelCase_ :	Tuple=0				,    UpperCamelCase_ :	Optional[Any]=0				,    UpperCamelCase_ :	List[Any]=True				,    **UpperCamelCase_ :	Optional[int]				,    ):





           '''simple docstring'''

           __magic_name__      				=    vocab_size
           __magic_name__      				=    decoder_vocab_size or vocab_size
           __magic_name__      				=    max_position_embeddings
           __magic_name__      				=    d_model
           __magic_name__      				=    encoder_ffn_dim
           __magic_name__      				=    encoder_layers
           __magic_name__      				=    encoder_attention_heads
           __magic_name__      				=    decoder_ffn_dim
           __magic_name__      				=    decoder_layers
           __magic_name__      				=    decoder_attention_heads
           __magic_name__      				=    dropout
           __magic_name__      				=    attention_dropout
           __magic_name__      				=    activation_dropout
           __magic_name__      				=    activation_function
           __magic_name__      				=    init_std
           __magic_name__      				=    encoder_layerdrop
           __magic_name__      				=    decoder_layerdrop
           __magic_name__      				=    use_cache
           __magic_name__      				=    encoder_layers
           __magic_name__      				=    scale_embedding  # scale factor will be sqrt(d_model) if True
           __magic_name__      				=    share_encoder_decoder_embeddings
           super().__init__(
               pad_token_id=UpperCamelCase_				,    eos_token_id=UpperCamelCase_				,    is_encoder_decoder=UpperCamelCase_				,    decoder_start_token_id=UpperCamelCase_				,    forced_eos_token_id=UpperCamelCase_				,    **UpperCamelCase_				,    )



class        UpperCamelCase__ (				a_):







    """simple docstring"""







    @property
    # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
    def 				a__			(		self :	Dict ):





           '''simple docstring'''

           if self.task in ["default", "seq2seq-lm"]:
                  __magic_name__      				=    OrderedDict(
                      [
                          ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                          ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                      ] )

                  if self.use_past:
                         __magic_name__      				=    {0: 'batch'}
                         __magic_name__      				=    {0: 'batch', 1: 'past_decoder_sequence + sequence'}
                  else:
                         __magic_name__      				=    {0: 'batch', 1: 'decoder_sequence'}
                         __magic_name__      				=    {0: 'batch', 1: 'decoder_sequence'}

                  if self.use_past:
                         self.fill_with_past_key_values_(UpperCamelCase_				,    direction='inputs' )
           elif self.task == "causal-lm":
                  # TODO: figure this case out.
                  __magic_name__      				=    OrderedDict(
                      [
                          ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                          ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                      ] )
                  if self.use_past:
                         __magic_name__				,  __magic_name__      				=    self.num_layers
                         for i in range(UpperCamelCase_ ):
                                __magic_name__      				=    {0: 'batch', 2: 'past_sequence + sequence'}
                                __magic_name__      				=    {0: 'batch', 2: 'past_sequence + sequence'}
           else:
                  __magic_name__      				=    OrderedDict(
                      [
                          ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                          ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                          ('decoder_input_ids', {0: 'batch', 1: 'decoder_sequence'}),
                          ('decoder_attention_mask', {0: 'batch', 1: 'decoder_sequence'}),
                      ] )

           return common_inputs







    @property
    # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
    def 				a__			(		self :	Any ):





           '''simple docstring'''

           if self.task in ["default", "seq2seq-lm"]:
                  __magic_name__      				=    super().outputs
           else:
                  __magic_name__      				=    super(UpperCamelCase_				,    self ).outputs
                  if self.use_past:
                         __magic_name__				,  __magic_name__      				=    self.num_layers
                         for i in range(UpperCamelCase_ ):
                                __magic_name__      				=    {0: 'batch', 2: 'past_sequence + sequence'}
                                __magic_name__      				=    {0: 'batch', 2: 'past_sequence + sequence'}
           return common_outputs







    def 				a__			(		self :	Union[str, Any]				,    UpperCamelCase_ :	PreTrainedTokenizer				,    UpperCamelCase_ :	int = -1				,    UpperCamelCase_ :	int = -1				,    UpperCamelCase_ :	bool = False				,    UpperCamelCase_ :	Optional[TensorType] = None				,    ):





           '''simple docstring'''

           __magic_name__      				=    self._generate_dummy_inputs_for_encoder_and_decoder(
               UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_ )

           # Generate decoder inputs
           __magic_name__      				=    seq_length if not self.use_past else 1
           __magic_name__      				=    self._generate_dummy_inputs_for_encoder_and_decoder(
               UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_ )
           __magic_name__      				=    {f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
           __magic_name__      				=    dict(**UpperCamelCase_				,    **UpperCamelCase_ )

           if self.use_past:
                  if not is_torch_available():
                         raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.' )
                  else:
                         import torch
                  __magic_name__				,  __magic_name__      				=    common_inputs['input_ids'].shape
                  __magic_name__      				=    common_inputs['decoder_input_ids'].shape[1]
                  __magic_name__				,  __magic_name__      				=    self.num_attention_heads
                  __magic_name__      				=    (
                      batch,
                      num_encoder_attention_heads,
                      encoder_seq_length,
                      self._config.hidden_size // num_encoder_attention_heads,
                  )
                  __magic_name__      				=    decoder_seq_length + 3
                  __magic_name__      				=    (
                      batch,
                      num_decoder_attention_heads,
                      decoder_past_length,
                      self._config.hidden_size // num_decoder_attention_heads,
                  )

                  __magic_name__      				=    torch.cat(
                      [common_inputs['decoder_attention_mask'], torch.ones(UpperCamelCase_				,    UpperCamelCase_ )]				,    dim=1 )

                  __magic_name__      				=    []
                  # If the number of encoder and decoder layers are present in the model configuration, both are considered
                  __magic_name__				,  __magic_name__      				=    self.num_layers
                  __magic_name__      				=    min(UpperCamelCase_				,    UpperCamelCase_ )
                  __magic_name__      				=    max(UpperCamelCase_				,    UpperCamelCase_ ) - min_num_layers
                  __magic_name__      				=    'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

                  for _ in range(UpperCamelCase_ ):
                         common_inputs["past_key_values"].append(
                             (
                                 torch.zeros(UpperCamelCase_ ),
                                 torch.zeros(UpperCamelCase_ ),
                                 torch.zeros(UpperCamelCase_ ),
                                 torch.zeros(UpperCamelCase_ ),
                             ) )
                  # TODO: test this.
                  __magic_name__      				=    encoder_shape if remaining_side_name == 'encoder' else decoder_shape
                  for _ in range(UpperCamelCase_				,    UpperCamelCase_ ):
                         common_inputs["past_key_values"].append((torch.zeros(UpperCamelCase_ ), torch.zeros(UpperCamelCase_ )) )
           return common_inputs







    def 				a__			(		self :	int				,    UpperCamelCase_ :	PreTrainedTokenizer				,    UpperCamelCase_ :	int = -1				,    UpperCamelCase_ :	int = -1				,    UpperCamelCase_ :	bool = False				,    UpperCamelCase_ :	Optional[TensorType] = None				,    ):





           '''simple docstring'''

           __magic_name__      				=    self._generate_dummy_inputs_for_encoder_and_decoder(
               UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_ )

           if self.use_past:
                  if not is_torch_available():
                         raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.' )
                  else:
                         import torch
                  __magic_name__				,  __magic_name__      				=    common_inputs['input_ids'].shape
                  # Not using the same length for past_key_values
                  __magic_name__      				=    seqlen + 2
                  __magic_name__				,  __magic_name__      				=    self.num_layers
                  __magic_name__				,  __magic_name__      				=    self.num_attention_heads
                  __magic_name__      				=    (
                      batch,
                      num_encoder_attention_heads,
                      past_key_values_length,
                      self._config.hidden_size // num_encoder_attention_heads,
                  )

                  __magic_name__      				=    common_inputs['attention_mask'].dtype
                  __magic_name__      				=    torch.cat(
                      [common_inputs['attention_mask'], torch.ones(UpperCamelCase_				,    UpperCamelCase_				,    dtype=UpperCamelCase_ )]				,    dim=1 )
                  __magic_name__      				=    [
                      (torch.zeros(UpperCamelCase_ ), torch.zeros(UpperCamelCase_ )) for _ in range(UpperCamelCase_ )
                  ]
           return common_inputs







    def 				a__			(		self :	List[str]				,    UpperCamelCase_ :	PreTrainedTokenizer				,    UpperCamelCase_ :	int = -1				,    UpperCamelCase_ :	int = -1				,    UpperCamelCase_ :	bool = False				,    UpperCamelCase_ :	Optional[TensorType] = None				,    ):





           '''simple docstring'''

           __magic_name__      				=    compute_effective_axis_dimension(
               UpperCamelCase_				,    fixed_dimension=OnnxConfig.default_fixed_batch				,    num_token_to_add=0 )

           # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
           __magic_name__      				=    tokenizer.num_special_tokens_to_add(UpperCamelCase_ )
           __magic_name__      				=    compute_effective_axis_dimension(
               UpperCamelCase_				,    fixed_dimension=OnnxConfig.default_fixed_sequence				,    num_token_to_add=UpperCamelCase_ )

           # Generate dummy inputs according to compute batch and sequence
           __magic_name__      				=    [' '.join([tokenizer.unk_token] ) * seq_length] * batch_size
           __magic_name__      				=    dict(tokenizer(UpperCamelCase_				,    return_tensors=UpperCamelCase_ ) )
           return common_inputs







    def 				a__			(		self :	List[Any]				,    UpperCamelCase_ :	PreTrainedTokenizer				,    UpperCamelCase_ :	int = -1				,    UpperCamelCase_ :	int = -1				,    UpperCamelCase_ :	bool = False				,    UpperCamelCase_ :	Optional[TensorType] = None				,    ):





           '''simple docstring'''

           if self.task in ["default", "seq2seq-lm"]:
                  __magic_name__      				=    self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                      UpperCamelCase_				,    batch_size=UpperCamelCase_				,    seq_length=UpperCamelCase_				,    is_pair=UpperCamelCase_				,    framework=UpperCamelCase_ )

           else:
                  __magic_name__      				=    self._generate_dummy_inputs_for_causal_lm(
                      UpperCamelCase_				,    batch_size=UpperCamelCase_				,    seq_length=UpperCamelCase_				,    is_pair=UpperCamelCase_				,    framework=UpperCamelCase_ )

           return common_inputs







    def 				a__			(		self :	Union[str, Any]				,    UpperCamelCase_ :	Dict				,    UpperCamelCase_ :	int				,    UpperCamelCase_ :	Optional[Any]				,    UpperCamelCase_ :	Tuple ):





           '''simple docstring'''

           if self.task in ["default", "seq2seq-lm"]:
                  __magic_name__      				=    super()._flatten_past_key_values_(UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_ )
           else:
                  __magic_name__      				=    super(UpperCamelCase_				,    self )._flatten_past_key_values_(
                      UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_ )







    @property
    def 				a__			(		self :	Optional[int] ):





           '''simple docstring'''

           return 1e-4







"""simple docstring"""
def 							A						(				__snake_case: str  )    ->		list:

       """simple docstring"""





       return [
           txt[:a] + txt[a].upper() + txt[a + 1 :]
           for a in range(len(__snake_case  )  )
           if txt[a].isalpha()
       ]


if __name__ == "__main__":
 __import__("""doctest""").testmod()